Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must cover behavioral traits. It mentions possible statuses and a result URL upon completion, adding some transparency. But it does not disclose read-only nature, idempotency, or potential side effects, which are important for a status-checking tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured with an opening statement, explicit Args and Returns sections, and examples. Every sentence adds value, and the most critical info is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (though not shown), the description still covers return values (statuses and result URL). It completely explains both parameters, usage scenarios, and provides examples, making it fully adequate for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds meaning for both parameters: it describes the format of job_id (UUID example) and explains the endpoint_type enum values with their API associations. This compensates well beyond the schema's minimal type definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks the status of a job, specifying both image generation (Seedream) and editing (Nano Banana). It uses a specific verb 'check' and resource 'job status', and distinguishes from siblings like generate_image and edit_image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use it for monitoring long-running jobs or checking on timed-out jobs. It indicates it works for both endpoints. However, it lacks explicit when-not-to-use guidance or alternative tool referrals, though the examples provide context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavior: returns URL/base64, supports sync/async modes, cost per resolution, and return format. It does not mention side effects like whether original images are preserved, but overall it is transparent about key behaviors.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is organized with Args, Returns, and Examples sections, making it easy to parse. It is somewhat lengthy but each sentence adds value (e.g., parameter details, cost info, return format). Could be slightly trimmed without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (8 parameters, no annotations, but presence of output schema), the description is highly complete. It covers all parameters, provides usage examples, explains output format, and mentions cost. The agent has sufficient information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage (no parameter descriptions), but the tool description compensates fully by detailing each parameter: prompt with examples, image_urls constraints, resolution options with costs, aspect_ratio list, output_format, and boolean/integer flags. This adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool edits or transforms images using an API, distinguishing it from siblings like generate_image (creates new images) and check_job_status (checks job status). The verb 'edit' and resource 'images' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides examples of when to use (e.g., 'Add dramatic sunset lighting'), implying the tool is for editing existing images. However, it lacks explicit when-not-to-use guidance or direct alternatives (e.g., 'for creating new images, use generate_image'). The context is clear but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses key behaviors: async processing, safety checker feature, seed reproducibility, and return format. It could mention failure modes or rate limits, but current coverage is good.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear intro, organized Args section, Returns format, and examples. Each sentence adds value, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no annotations, the description covers purpose, all parameters with semantics, behavioral traits (async, safety), return format, and examples. It provides sufficient context for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides detailed semantics for all 6 parameters, including valid ranges, defaults, and usage tips (e.g., negative_prompt recommended, seed for reproducibility). This adds high value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates images from text descriptions using Seedream V4 T2I. It specifies high-quality photorealistic or artistic output, and distinguishes from siblings by focusing on generation vs. status checking or editing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on async processing and typical completion time, but does not explicitly state when to avoid using this tool or compare directly with sibling tools like edit_image. Examples help suggest appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full responsibility. It discloses that the tool returns formatted reference information and that the api parameter defaults to 'all.' However, it does not explicitly state that the tool has no side effects, does not modify state, or discuss rate limits or authentication. For a read-only reference tool, this is minimally adequate but could be improved.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with Args, Returns, and Examples sections. It front-loads the core purpose in the first sentence. Every part is informative and earns its place, with no extraneous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 optional parameter, no output schema missing), the description covers all needed aspects: parameter options and defaults, return content description, and usage examples. It is complete for an agent to understand and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the input schema has 0% description coverage, the description fully explains the api parameter: its default value and each enum option (seedream, nano_banana, all) with brief explanations. This compensates completely for the schema gap, adding significant meaning beyond the schema structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get information about supported parameters and capabilities.' It specifies the resource (API info) and action (get). The tool is distinct from siblings like generate_image or edit_image, which focus on image manipulation, making its role unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for reference and exploration, e.g., 'provides reference information about what each API supports.' It does not explicitly state when not to use or directly compare with siblings, but the context and examples (e.g., 'Get all API info') make the use case clear. A slight improvement would be to mention it is read-only and safe to call at any time.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.